SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images
Change detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts this research on faster and more accurate change detection methods. We propose a pixel-level semantic change detection method t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/24/5631 |
| _version_ | 1797379514101137408 |
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| author | Lili Zhang Mengqi Xu Gaoxu Wang Rui Shi Yi Xu Ruijie Yan |
| author_facet | Lili Zhang Mengqi Xu Gaoxu Wang Rui Shi Yi Xu Ruijie Yan |
| author_sort | Lili Zhang |
| collection | DOAJ |
| description | Change detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts this research on faster and more accurate change detection methods. We propose a pixel-level semantic change detection method to solve the fine-grained semantic change detection for HR remote sensing image pairs, which takes one lightweight semantic segmentation network (LightNet), using the parameter-sharing SiameseNet, as the architecture to carry out pixel-level semantic segmentations for the dual-temporal image pairs and achieve pixel-level change detection based directly on semantic comparison. LightNet consists of four long–short branches, each including lightweight dilated residual blocks and an information enhancement module. The feature information is transmitted, fused, and enhanced among the four branches, where two large-scale feature maps are fused and then enhanced via the channel information enhancement module. The two small-scale feature maps are fused and then enhanced via a spatial information enhancement module, and the four upsampling feature maps are finally concatenated to form the input of the Softmax. We used high resolution remote sensing images of Lake Erhai in Yunnan Province in China, collected by GF-2, to make one dataset with a fine-grained semantic label and a dual-temporal image-pair label to train our model, and the experiments demonstrate the superiority of our method and the accuracy of LightNet; the pixel-level semantic change detection methods are up to 89% and 86%, respectively. |
| first_indexed | 2024-03-08T20:24:23Z |
| format | Article |
| id | doaj.art-ad3217f9ac444dd9b6c2d84d0ad9be07 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T20:24:23Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ad3217f9ac444dd9b6c2d84d0ad9be072023-12-22T14:38:47ZengMDPI AGRemote Sensing2072-42922023-12-011524563110.3390/rs15245631SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing ImagesLili Zhang0Mengqi Xu1Gaoxu Wang2Rui Shi3Yi Xu4Ruijie Yan5College of Information Science and Engineering, Hohai University, Nanjing 211100, ChinaCollege of Computer and Software, Hohai University, Nanjing 211100, ChinaState Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaState Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaState Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaCollege of Computer and Software, Hohai University, Nanjing 211100, ChinaChange detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts this research on faster and more accurate change detection methods. We propose a pixel-level semantic change detection method to solve the fine-grained semantic change detection for HR remote sensing image pairs, which takes one lightweight semantic segmentation network (LightNet), using the parameter-sharing SiameseNet, as the architecture to carry out pixel-level semantic segmentations for the dual-temporal image pairs and achieve pixel-level change detection based directly on semantic comparison. LightNet consists of four long–short branches, each including lightweight dilated residual blocks and an information enhancement module. The feature information is transmitted, fused, and enhanced among the four branches, where two large-scale feature maps are fused and then enhanced via the channel information enhancement module. The two small-scale feature maps are fused and then enhanced via a spatial information enhancement module, and the four upsampling feature maps are finally concatenated to form the input of the Softmax. We used high resolution remote sensing images of Lake Erhai in Yunnan Province in China, collected by GF-2, to make one dataset with a fine-grained semantic label and a dual-temporal image-pair label to train our model, and the experiments demonstrate the superiority of our method and the accuracy of LightNet; the pixel-level semantic change detection methods are up to 89% and 86%, respectively.https://www.mdpi.com/2072-4292/15/24/5631change detectiondual-temporal remote sensing imagesinformation enhancementSiamese network |
| spellingShingle | Lili Zhang Mengqi Xu Gaoxu Wang Rui Shi Yi Xu Ruijie Yan SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images Remote Sensing change detection dual-temporal remote sensing images information enhancement Siamese network |
| title | SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images |
| title_full | SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images |
| title_fullStr | SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images |
| title_full_unstemmed | SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images |
| title_short | SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images |
| title_sort | siamesenet based fine grained semantic change detection for high resolution remote sensing images |
| topic | change detection dual-temporal remote sensing images information enhancement Siamese network |
| url | https://www.mdpi.com/2072-4292/15/24/5631 |
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